Robust mean estimation for high-dimensional data in the presence of adversarial corruption is a well-studied problem in robust statistics. In this talk, we present an algorithm that tackles robust mean estimation by first reducing the problem to a meta-problem, and reformulating the original task into a framework that isolates the key statistical properties. Once the meta-problem is established, we employ a multiplicative weight update method to solve it. This computationally efficient iterative approach reweights the data points based on their consistency with the current estimate, effectively diminishing the influence of corrupted samples